Speaker
Description
The mechanical characterisation of ultrasoft materials, such as brain tissue, over a wide time range is often limited by inconsistent responses when using different experimental techniques. These inconsistencies are predominantly attributable to the disparate testing conditions across the experiments. However, multi-modality tests are essential to calibrate a model that is applied to finite element simulation in the time and frequency domains. Consequently, to achieve reliable mechanical parameters, a robust identification strategy for all experiments with distinct time scales is required.
This study aims to identify and combine the viscoelastic material parameters obtained from experiments conducted in the time and frequency domain. A phantom material based on oxidized hyaluronic acid (OHA) and gelatin (GEL), showing potential to mimic the viscoelastic behavior of brain tissue, was examined via three testing techniques. Quasi-static experiments at the rheometer provide insight into the time behaviour of the hydrogel. The material response is studied through the nonlinear deformation of the sample in compression and torsional shear. Via a vibration table the frequency-dependent behaviour in the medium range, from 20 to 200Hz, is analysed. This technique enables to study of the oscillation response of a hydrogel sample under excited vibration. The material response in the high-frequency domain is investigated with magnetic resonance elastography. A 0.5T magnet measured the vibrations induced in the material by a piezoelectric actuator, enabling the acquisition of data for frequencies up to several kHz.
A finite element simulation of the experiments in Abaqus was employed to determine the mechanical parameters of the OHA-GEL hydrogel. For the calibration, a hyperelastic Ogden model in combination with a viscoelastic Prony series was selected. To capture both the time and the frequency response of the hydrogel, a combination of multiple testing modalities was integrated into the inverse material parameter identification process.
